An Approximation Algorithm for Diagnostic Test Scheduling in Multicomputer Systems
IEEE Transactions on Computers
Complexity of scheduling parallel task systems
SIAM Journal on Discrete Mathematics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks
Journal of Scheduling
Combining competitive scheme with slack neurons to solve real-time job scheduling problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
A controlled genetic algorithm by fuzzy logic and belief functionsfor job-shop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-based discrete dynamic programming approach for scheduling inFMS environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An algorithm of task scheduling in survivability
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Hi-index | 12.05 |
Most publications in shop scheduling area focus on the static scheduling problems and seldom take into account the dynamic disturbances such as machine breakdown or new job arrivals. Motivated by the computational complexity of the scheduling problems, genetic algorithms (GAs) have been applied to improve both the efficiency and the effectiveness for NP-hard optimization problems. However, a pure GA-based approach tends to generate illegal schedules due to the crossover and the mutation operators. It is often the case that the gene expression or the genetic operators need to be specially tailored to fit the problem domain or some other schemes may be combined to solve the scheduling problems. This study presents a GA-based approach combined with a feasible energy function for multiprocessor scheduling problems with resource and timing constraints in dynamic real-time scheduling. Moreover, an easy-understood genotype is designed to generate legal schedules. The results of the experiments demonstrate that the proposed approach performs rapid convergence to address its applicability and generate good-quality schedules.